QUESTION 1Note: This question is part of a series of questions that present the same scenario. Each question in the series contains a unique solution that might meet the stated goals. Some question sets might have more than one correct solution, while others might not have a correct solution. After you answer a question in this sections, you will NOT be able to return to it. As a result, these questions will not appear in the review screen.You are working on an Azure Machine Learning experiment.You have the dataset configured as shown in the following table.

You need to ensure that you can compare the performance of the models and add annotations to the results.Solution: You connect the Score Model modules from each trained model as inputs for the Evaluate Model module, and then save the results as a dataset.Does this meet the goal?

QUESTION 2Note: This question is part of a series of questions that present the same scenario. Each question in the series contains a unique solution that might meet the stated goals. Some question sets might have more than one correct solution, while others might not have a correct solution. After you answer a question in this sections, you will NOT be able to return to it. As a result, these questions will not appear in the review screen.You are working on an Azure Machine Learning experiment.You have the dataset configured as shown in the following table.

You need to ensure that you can compare the performance of the models and add annotations to the results.Solution: You connect the Score Model modules from each trained model as inputs for the Evaluate Model module, and use the Execute R Script module.Does this meet the goal?

QUESTION 3Note: This question is part of a series of questions that use the same scenario. For your convenience, the scenario is repeated in each question. Each question presents a different goal and answer choices, but the text of the scenario is exactly the same in each question in this series.A travel agency named Margie’s Travel sells airline tickets to customers in the United States.Margie’s Travel wants you to provide insights and predictions on flight delays. The agency is considering implementing a system that will communicate to its customers as the flight departure nears about possible delays due to weather conditions. The flight data contains the following attributes: DepartureDate: The departure date aggregated at a per hour granularity Carrier: The code assigned by the IATA and commonly used to identify a carrier OriginAirportID: An identification number assigned by the USDOT to identify a unique airport (the flight’s norigin) DestAirportID: An identification number assigned by the USDOT to identify a unique airport (the flight’s destination) DepDel: The departure delay in minutes DepDel30: A Boolean value indicating whether the departure was delayed by 30 minutes or more (a value of 1 indicates that the departure was delayed by 30 minutes or more)The weather data contains the following attributes: AirportID, ReadingDate (YYYY/MM/DD HH), SkyConditionVisibility, WeatherType, WindSpeed, StationPressure, PressureChange, and HourlyPrecip.You need to use historical data about on-time flight performance and the weather data to predict whether the departure of a scheduled flight will be delayed by more than 30 minutes.Which method should you use?

QUESTION 4Note: This question is part of a series of questions that use the same scenario. For your convenience, the scenario is repeated in each question. Each question presents a different goal and answer choices, but the text of the scenario is exactly the same in each question in this series.A travel agency named Margie’s Travel sells airline tickets to customers in the United States.Margie’s Travel wants you to provide insights and predictions on flight delays. The agency is considering implementing a system that will communicate to its customers as the flight departure nears about possible delays due to weather conditions. The flight data contains the following attributes: DepartureDate: The departure date aggregated at a per hour granularity Carrier: The code assigned by the IATA and commonly used to identify a carrier OriginAirportID: An identification number assigned by the USDOT to identify a unique airport (the flight’s norigin) DestAirportID: An identification number assigned by the USDOT to identify a unique airport (the flight’s destination) DepDel: The departure delay in minutes DepDel30: A Boolean value indicating whether the departure was delayed by 30 minutes or more (a value of 1 indicates that the departure was delayed by 30 minutes or more)The weather data contains the following attributes: AirportID, ReadingDate (YYYY/MM/DD HH), SkyConditionVisibility, WeatherType, WindSpeed, StationPressure, PressureChange, and HourlyPrecip.You have an untrained Azure Machine Learning model that you plan to train to predict flight delays.You need to assess the variability of the dataset and the reliability of the predictions from the model.Which module should you use?

QUESTION 5Note: This question is part of a series of questions that use the same scenario. For your convenience, the scenario is repeated in each question. Each question presents a different goal and answer choices, but the text of the scenario is exactly the same in each question in this series.A travel agency named Margie’s Travel sells airline tickets to customers in the United States.Margie’s Travel wants you to provide insights and predictions on flight delays. The agency is considering implementing a system that will communicate to its customers as the flight departure nears about possible delays due to weather conditions. The flight data contains the following attributes: DepartureDate: The departure date aggregated at a per hour granularity Carrier: The code assigned by the IATA and commonly used to identify a carrier OriginAirportID: An identification number assigned by the USDOT to identify a unique airport (the flight’s norigin) DestAirportID: An identification number assigned by the USDOT to identify a unique airport (the flight’s destination) DepDel: The departure delay in minutes DepDel30: A Boolean value indicating whether the departure was delayed by 30 minutes or more (a value of 1 indicates that the departure was delayed by 30 minutes or more)The weather data contains the following attributes: AirportID, ReadingDate (YYYY/MM/DD HH), SkyConditionVisibility, WeatherType, WindSpeed, StationPressure, PressureChange, and HourlyPrecip.You plan to predict flight delays that are 30 minutes or more.You need to build a training model that accurately fits the data. The solution must minimize over fitting and minimize data leakage.Which attribute should you remove?

A. OriginAirportIDB. DepDelC. DepDel30D. CarrierE. DestAirportID

Answer: C

QUESTION 6Note: This question is part of a series of questions that use the same or similar answer choices. An answer choice may be correct for more than one question in the series. Each question is independent of the other questions in this series. Information and details provided in a question apply only to that question.You need to remove rows that have an empty value in a specific column. The solution must use a native module.Which module should you use?

QUESTION 7Note: This question is part of a series of questions that use the same or similar answer choices. An answer choice may be correct for more than one question in the series. Each question is independent of the other questions in this series. Information and details provided in a question apply only to that question.You have a non-tabular file that is saved in Azure Blob storage.You need to download the file locally, access the data in the file, and then format the data as a dataset.Which module should you use?

QUESTION 8Note: This question is part of a series of questions that use the same or similar answer choices. An answer choice may be correct for more than one question in the series. Each question is independent of the other questions in this series. Information and details provided in a question apply only to that question.You have a dataset that contains a column named Column1. Column1 is empty.You need to omit Column1 from the dataset. The solution must use a native module.Which module should you use?

QUESTION 9Note: This question is part of a series of questions that use the same or similar answer choices. An answer choice may be correct for more than one question in the series. Each question is independent of the other questions in this series. Information and details provided in a question apply only to that question.You need to use only one percent of an Apache Hive data table by conducting random sampling by groups.Which module should you use?

QUESTION 11You are building an Azure Machine Learning workflow by using Azure Machine Learning Studio.You create an Azure notebook that supports the Microsoft Cognitive Toolkit.You need to ensure that the stochastic gradient descent (SGD) configuration maximizes the samples per second and supports parallel modeling that is managed by a parameter server.Which SGD algorithm should you use?